Meta-analysis of RNA-seq expression data across species, tissues and studies.
Meta-analysis of RNA-seq expression data across species, tissues and studies.
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DOI：
10.1186/s13059-015-0853-4
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发表时间：
2015-12-22
期刊：
影响因子：
12.3
通讯作者：
Burge CB
中科院分区：
文献类型：
--
作者：
Sudmant PH;Alexis MS;Burge CB
基因表达的差异驱动物种之间的表型差异，但主要器官和组织通常具有保守的基因表达程序。一些比较转录组学研究已经观察到来自不同脊椎动物物种的同源组织之间的基因表达比同一物种的不同组织之间的基因表达更大的相似性。然而，林和同事最近的一项研究得出了相反的结论。这些研究在所分析的物种和组织以及文库制备、测序、读段作图、标准化、基因组和聚类方法的技术细节方面有所不同。为了更好地理解基因表达进化，我们重新分析了四项研究的数据，包括Lin的研究，使用标准化映射，归一化和聚类方法，包括11种脊椎动物的6-13种组织。对独立数据的分析表明，Lin等人选择的组织组比先前研究分析的组织组彼此更相似。比较四项研究中五种常见组织的表达，我们观察到样品仅按组织而不是按物种或研究聚类，支持哺乳动物器官生理学的保护。此外，同源组织之间的研究间距离通常小于不同组织之间的研究内距离，从而能够进行信息荟萃分析。值得注意的是，当将组织随时间的表达差异与51种人GTEx组织中的表达变化进行比较时，我们可以准确地预测任意组织和物种对的表达聚类。这些结果为未来基因表达进化研究的设计提供了一个框架，并证明了跨研究比较RNA-seq数据的实用性。本文的在线版本（doi：10.1186/s13059-015-0853-4）包含补充材料，可供授权用户使用。
Differences in gene expression drive phenotypic differences between species, yet major organs and tissues generally have conserved gene expression programs. Several comparative transcriptomic studies have observed greater similarity in gene expression between homologous tissues from different vertebrate species than between diverse tissues of the same species. However, a recent study by Lin and colleagues reached the opposite conclusion. These studies differed in the species and tissues analyzed, and in technical details of library preparation, sequencing, read mapping, normalization, gene sets, and clustering methods. To better understand gene expression evolution we reanalyzed data from four studies, including that of Lin, encompassing 6–13 tissues each from 11 vertebrate species using standardized mapping, normalization, and clustering methods. An analysis of independent data showed that the set of tissues chosen by Lin et al. were more similar to each other than those analyzed by previous studies. Comparing expression in five common tissues from the four studies, we observed that samples clustered exclusively by tissue rather than by species or study, supporting conservation of organ physiology in mammals. Furthermore, inter-study distances between homologous tissues were generally less than intra-study distances among different tissues, enabling informative meta-analyses. Notably, when comparing expression divergence of tissues over time to expression variation across 51 human GTEx tissues, we could accurately predict the clustering of expression for arbitrary pairs of tissues and species. These results provide a framework for the design of future evolutionary studies of gene expression and demonstrate the utility of comparing RNA-seq data across studies. The online version of this article (doi:10.1186/s13059-015-0853-4) contains supplementary material, which is available to authorized users.